Enterprise Data Visualization Through Multi-Platform Data Integration
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Solution Overview
Problem
Existing data processing and visualization methods for enterprise management lack timely and complete data acquisition, suffer from discrepancies in indicator data, require dynamic updates, and lack effective analysis and intuitive display methods, leading to low accuracy and inefficiency.
Innovation Solution
A data processing and visualization method involving data acquisition from multiple platforms at different time points, segmentation, and integration into a multi-dimensional data model, coupled with visualization tools to create visual graphs representing enterprise capacity and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is acquired from multiple platforms at different time points, then data completeness and timeliness are improved, but data processing complexity increases
Solution Approach 1:
The patent segments data acquisition by dividing it into multiple data platforms and time points, allowing systematic collection of comprehensive enterprise data while managing complexity through structured organization of data sources
Solution Approach 2:
The patent introduces a data processing system as an intermediary that automatically collects, cleans, and integrates data from multiple platforms, reducing the complexity burden on users while ensuring data completeness and timeliness
2Measurement precision
If non-recurring gains and losses are deducted to improve analysis accuracy, then enterprise performance measurement becomes more accurate, but calculation complexity increases
Solution Approach 1:
The patent extracts non-recurring gains and losses from enterprise financial data, separating temporary anomalies from core operational performance to improve measurement accuracy while using automated algorithms to manage calculation complexity
Solution Approach 2:
The patent performs preliminary identification and classification of non-recurring items before final performance calculation, preparing data in advance to reduce complexity during the actual analysis process
3Adaptability or versatility
If multi-dimensional data models are created, then analysis comprehensiveness is improved, but system complexity increases
Solution Approach 1:
The patent creates multi-dimensional data models that analyze enterprise performance across multiple dimensions (operational, financial, market), improving comprehensiveness while using standardized modeling frameworks to control system complexity
4Ease of operation
If visual graphs are generated to display enterprise performance, then data display effectiveness is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary data processing and model creation in advance, preparing visual graph data structures beforehand to reduce processing time when actual visualization is needed while maintaining display effectiveness
Data Source
AI summary
The present disclosure relates to the technical field of big data and artificial intelligence. Disclosed are a data processing and visualization method, medium and device. The method includes: selecting a data platform to acquire source data of a target enterprise, sorting the source data and loading the source data into a data system to serve as a source database table, executing a primary data operation to process the source data into target data, executing a secondary data operation to process the target data into value-added data, executing a tertiary data operation to process relevant data into graphic data, and executing data visualization, so as to draw and render a visual graph by using the graphic data.